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dc.contributor.authorPuteh, Saad-
dc.contributor.authorMohamed Rizon, Mohamed Juhari-
dc.contributor.authorNor Khairah, Jamaludin-
dc.contributor.authorSiti Sakira, Kamarudin-
dc.contributor.authorAryati, Bakri-
dc.contributor.authorNursalasawati, Rusli-
dc.date.accessioned2009-07-10T07:55:28Z-
dc.date.available2009-07-10T07:55:28Z-
dc.date.issued2004-01-28-
dc.identifier.citationp.148-151en_US
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/6424-
dc.descriptionOrganized by Oita University, 28th - 30th January 2004 at Beppu, Oita, Japan.en_US
dc.description.abstractParameters that affect rice yield are many, for instance diseases, pests and weeds. Statistical or mathematical model is unable to describe the correlation between plant diseases, pests and weeds on the amount of rice yield. In this study, a Backpropogation (BP) algorithm is utilized to develop a neural network model to predict rice yield based on the aforementioned factors in MUDA irrigation area, Malaysia. The result of this study shows that the BP algorithm is able to predict the rice yield to a deviation of less than 0.21.en_US
dc.language.isoenen_US
dc.publisherOita Universityen_US
dc.relation.ispartofseriesProceedings of the 9th International Symposium on Artificial Life and Robotics (AROB 9th '04)en_US
dc.subjectBPen_US
dc.subjectRice yield predictionen_US
dc.subjectImproved unit rangeen_US
dc.subjectRice yielden_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectBack propagationen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer programmingen_US
dc.titleBackpropagation algorithm for rice yield predictionen_US
dc.typeWorking Paperen_US
Appears in Collections:Conference Papers
Mohd. Rizon Mohamed Juhari, Prof. Ir. Dr.

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